"""simple docstring"""



from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin






@dataclass
class  snake_case__      (       snake_case_       ):
 _snake_case       :					torch.FloatTensor






class  snake_case__      (       snake_case_,						snake_case_       ):


 @register_to_config
 def __init__( self    ,		lowerCamelCase = 32    ,		lowerCamelCase = 64    ,		lowerCamelCase = 20    ,		lowerCamelCase = 768    ,		lowerCamelCase=77    ,		lowerCamelCase=4    ,		lowerCamelCase = 0.0    ,		lowerCamelCase = "silu"    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = "linear"    ,		lowerCamelCase = "prd"    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		):
        super().__init__()
        __a       =    num_attention_heads
        __a       =    attention_head_dim
        __a       =    num_attention_heads * attention_head_dim
        __a       =    additional_embeddings

        __a       =    time_embed_dim or inner_dim
        __a       =    embedding_proj_dim or embedding_dim
        __a       =    clip_embed_dim or embedding_dim

        __a       =    Timesteps(lowerCamelCase    ,		lowerCamelCase    ,		0						)
        __a       =    TimestepEmbedding(lowerCamelCase    ,		lowerCamelCase    ,		out_dim=lowerCamelCase    ,		act_fn=lowerCamelCase						)

        __a       =    nn.Linear(lowerCamelCase    ,		lowerCamelCase						)

        if embedding_proj_norm_type is None:
               __a       =    None
        elif embedding_proj_norm_type == "layer":
               __a       =    nn.LayerNorm(lowerCamelCase						)
        else:
               raise ValueError(F"unsupported embedding_proj_norm_type: {embedding_proj_norm_type}"						)

        __a       =    nn.Linear(lowerCamelCase    ,		lowerCamelCase						)

        if encoder_hid_proj_type is None:
               __a       =    None
        elif encoder_hid_proj_type == "linear":
               __a       =    nn.Linear(lowerCamelCase    ,		lowerCamelCase						)
        else:
               raise ValueError(F"unsupported encoder_hid_proj_type: {encoder_hid_proj_type}"						)

        __a       =    nn.Parameter(torch.zeros(1    ,		num_embeddings + additional_embeddings    ,		lowerCamelCase						)						)

        if added_emb_type == "prd":
               __a       =    nn.Parameter(torch.zeros(1    ,		1    ,		lowerCamelCase						)						)
        elif added_emb_type is None:
               __a       =    None
        else:
               raise ValueError(
                   F"`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`."						)

        __a       =    nn.ModuleList(
            [
                BasicTransformerBlock(
                    lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		dropout=lowerCamelCase    ,		activation_fn="gelu"    ,		attention_bias=lowerCamelCase    ,		)
                for d in range(lowerCamelCase						)
            ]						)

        if norm_in_type == "layer":
               __a       =    nn.LayerNorm(lowerCamelCase						)
        elif norm_in_type is None:
               __a       =    None
        else:
               raise ValueError(F"Unsupported norm_in_type: {norm_in_type}."						)

        __a       =    nn.LayerNorm(lowerCamelCase						)

        __a       =    nn.Linear(lowerCamelCase    ,		lowerCamelCase						)

        __a       =    torch.full(
            [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]    ,		-1_0000.0						)
        causal_attention_mask.triu_(1						)
        __a       =    causal_attention_mask[None, ...]
        self.register_buffer("causal_attention_mask"    ,		lowerCamelCase    ,		persistent=lowerCamelCase						)

        __a       =    nn.Parameter(torch.zeros(1    ,		lowerCamelCase						)						)
        __a       =    nn.Parameter(torch.zeros(1    ,		lowerCamelCase						)						)


 @property
 # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
 def 						a__   ( self						):
        __a       =    {}

        def fn_recursive_add_processors(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
               if hasattr(lowerCamelCase    ,		"set_processor"						):
                      __a       =    module.processor

               for sub_name, child in module.named_children():
                      fn_recursive_add_processors(F"{name}.{sub_name}"    ,		lowerCamelCase    ,		lowerCamelCase						)

               return processors

        for name, module in self.named_children():
               fn_recursive_add_processors(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						)

        return processors


 def 						a__   ( self    ,		lowerCamelCase						):
        __a       =    len(self.attn_processors.keys()						)

        if isinstance(lowerCamelCase    ,		lowerCamelCase						) and len(lowerCamelCase						) != count:
               raise ValueError(
                   F"A dict of processors was passed, but the number of processors {len(lowerCamelCase						)} does not match the"
                   F" number of attention layers: {count}. Please make sure to pass {count} processor classes."						)

        def fn_recursive_attn_processor(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
               if hasattr(lowerCamelCase    ,		"set_processor"						):
                      if not isinstance(lowerCamelCase    ,		lowerCamelCase						):
                             module.set_processor(lowerCamelCase						)
                      else:
                             module.set_processor(processor.pop(F"{name}.processor"						)						)

               for sub_name, child in module.named_children():
                      fn_recursive_attn_processor(F"{name}.{sub_name}"    ,		lowerCamelCase    ,		lowerCamelCase						)

        for name, module in self.named_children():
               fn_recursive_attn_processor(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						)


 def 						a__   ( self						):
        self.set_attn_processor(AttnProcessor()						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = True    ,		):
        __a       =    hidden_states.shape[0]

        __a       =    timestep
        if not torch.is_tensor(lowerCamelCase						):
               __a       =    torch.tensor([timesteps]    ,		dtype=torch.long    ,		device=hidden_states.device						)
        elif torch.is_tensor(lowerCamelCase						) and len(timesteps.shape						) == 0:
               __a       =    timesteps[None].to(hidden_states.device						)

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        __a       =    timesteps * torch.ones(lowerCamelCase    ,		dtype=timesteps.dtype    ,		device=timesteps.device						)

        __a       =    self.time_proj(lowerCamelCase						)

        # timesteps does not contain any weights and will always return f32 tensors
        # but time_embedding might be fp16, so we need to cast here.
        __a       =    timesteps_projected.to(dtype=self.dtype						)
        __a       =    self.time_embedding(lowerCamelCase						)

        if self.embedding_proj_norm is not None:
               __a       =    self.embedding_proj_norm(lowerCamelCase						)

        __a       =    self.embedding_proj(lowerCamelCase						)
        if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
               __a       =    self.encoder_hidden_states_proj(lowerCamelCase						)
        elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
               raise ValueError("`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set"						)

        __a       =    self.proj_in(lowerCamelCase						)

        __a       =    self.positional_embedding.to(hidden_states.dtype						)

        __a       =    []
        __a       =    0

        if encoder_hidden_states is not None:
               additional_embeds.append(lowerCamelCase						)
               additional_embeddings_len += encoder_hidden_states.shape[1]

        if len(proj_embeddings.shape						) == 2:
               __a       =    proj_embeddings[:, None, :]

        if len(hidden_states.shape						) == 2:
               __a       =    hidden_states[:, None, :]

        __a       =    additional_embeds + [
            proj_embeddings,
            time_embeddings[:, None, :],
            hidden_states,
        ]

        if self.prd_embedding is not None:
               __a       =    self.prd_embedding.to(hidden_states.dtype						).expand(lowerCamelCase    ,		-1    ,		-1						)
               additional_embeds.append(lowerCamelCase						)

        __a       =    torch.cat(
            lowerCamelCase    ,		dim=1    ,		)

        # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
        __a       =    additional_embeddings_len + proj_embeddings.shape[1] + 1
        if positional_embeddings.shape[1] < hidden_states.shape[1]:
               __a       =    F.pad(
                   lowerCamelCase    ,		(
                       0,
                       0,
                       additional_embeddings_len,
                       self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
                   )    ,		value=0.0    ,		)

        __a       =    hidden_states + positional_embeddings

        if attention_mask is not None:
               __a       =    (1 - attention_mask.to(hidden_states.dtype						)) * -1_0000.0
               __a       =    F.pad(lowerCamelCase    ,		(0, self.additional_embeddings)    ,		value=0.0						)
               __a       =    (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype						)
               __a       =    attention_mask.repeat_interleave(self.config.num_attention_heads    ,		dim=0						)

        if self.norm_in is not None:
               __a       =    self.norm_in(lowerCamelCase						)

        for block in self.transformer_blocks:
               __a       =    block(lowerCamelCase    ,		attention_mask=lowerCamelCase						)

        __a       =    self.norm_out(lowerCamelCase						)

        if self.prd_embedding is not None:
               __a       =    hidden_states[:, -1]
        else:
               __a       =    hidden_states[:, additional_embeddings_len:]

        __a       =    self.proj_to_clip_embeddings(lowerCamelCase						)

        if not return_dict:
               return (predicted_image_embedding,)

        return PriorTransformerOutput(predicted_image_embedding=lowerCamelCase						)






 def 						a__   ( self    ,		lowerCamelCase						):
        __a       =    (prior_latents * self.clip_std) + self.clip_mean
        return prior_latents

"""simple docstring"""



import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

SCREAMING_SNAKE_CASE__:Union[str, Any]									=			2






class  snake_case__      :


 def __init__( self    ,		*,  # begin keyword-only arguments
     lowerCamelCase="<s>"    ,		lowerCamelCase="<pad>"    ,		lowerCamelCase="</s>"    ,		lowerCamelCase="<unk>"    ,		lowerCamelCase=None    ,		):
        __a ,   __a ,   __a ,   __a       =    bos, unk, pad, eos
        __a       =    []
        __a       =    []
        __a       =    {}
        __a       =    self.add_symbol(lowerCamelCase						)
        __a       =    self.add_symbol(lowerCamelCase						)
        __a       =    self.add_symbol(lowerCamelCase						)
        __a       =    self.add_symbol(lowerCamelCase						)
        if extra_special_symbols:
               for s in extra_special_symbols:
                      self.add_symbol(lowerCamelCase						)
        __a       =    len(self.symbols						)


 def __eq__( self    ,		lowerCamelCase						):
        return self.indices == other.indices


 def __getitem__( self    ,		lowerCamelCase						):
        if idx < len(self.symbols						):
               return self.symbols[idx]
        return self.unk_word


 def __len__( self						):
        return len(self.symbols						)


 def __contains__( self    ,		lowerCamelCase						):
        return sym in self.indices


 @classmethod
 def 						a__   ( cls    ,		lowerCamelCase						):
        __a       =    cls()
        d.add_from_file(lowerCamelCase						)
        return d


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase=1    ,		lowerCamelCase=False						):
        if word in self.indices and not overwrite:
               __a       =    self.indices[word]
               __a       =    self.count[idx] + n
               return idx
        else:
               __a       =    len(self.symbols						)
               __a       =    idx
               self.symbols.append(lowerCamelCase						)
               self.count.append(lowerCamelCase						)
               return idx


 def 						a__   ( self    ,		lowerCamelCase						):
        return 0






 def 						a__   ( self    ,		lowerCamelCase						):
        if isinstance(lowerCamelCase    ,		lowerCamelCase						):
               try:
                      with open(lowerCamelCase    ,		"r"    ,		encoding="utf-8"						) as fd:
                             self.add_from_file(lowerCamelCase						)
               except FileNotFoundError as fnfe:
                      raise fnfe
               except UnicodeError:
                      raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(lowerCamelCase						)						)
               return

        __a       =    f.readlines()
        __a       =    self._load_meta(lowerCamelCase						)

        for line in lines[indices_start_line:]:
               try:
                      __a ,   __a       =    line.rstrip().rsplit(" "    ,		1						)
                      if field == "#fairseq:overwrite":
                             __a       =    True
                             __a ,   __a       =    line.rsplit(" "    ,		1						)
                      else:
                             __a       =    False
                      __a       =    int(lowerCamelCase						)
                      __a       =    line
                      if word in self and not overwrite:
                             raise RuntimeError(
                                 "Duplicate word found when loading Dictionary: '{}'. "
                                 "Duplicate words can overwrite earlier ones by adding the "
                                 "#fairseq:overwrite flag at the end of the corresponding row "
                                 "in the dictionary file. If using the Camembert model, please "
                                 "download an updated copy of the model file.".format(lowerCamelCase						)						)
                      self.add_symbol(lowerCamelCase    ,		n=lowerCamelCase    ,		overwrite=lowerCamelCase						)
               except ValueError:
                      raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'"						)



def 	_lowerCamelCase(	a				):
       # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
       # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
       __a       =    dict((re.sub(R"@@$"   ,    ""   ,    a				), v) if k.endswith("@@"				) else (re.sub(R"$"   ,    "</w>"   ,    a				), v) for k, v in d.items()				)
       __a       =    "<s> <pad> </s> <unk>".split()
       # restore the special tokens
       for k in keep_keys:
              del da[F"{k}</w>"]
              __a       =    d[k]  # restore
       return da



def 	_lowerCamelCase(	a   ,    a				):
       # prep
       if not os.path.exists(a				):
              raise ValueError(F"path {biogpt_checkpoint_path} does not exist!"				)
       os.makedirs(a   ,    exist_ok=a				)
       print(F"Writing results to {pytorch_dump_folder_path}"				)

       # handle various types of models

       __a       =    os.path.join(a   ,    "checkpoint.pt"				)
       if not os.path.isfile(a				):
              raise ValueError(F"path to the file {checkpoint_file} does not exist!"				)
       __a       =    torch.load(a   ,    map_location="cpu"				)

       __a       =    chkpt["cfg"]["model"]

       # dicts
       __a       =    os.path.join(a   ,    "dict.txt"				)
       if not os.path.isfile(a				):
              raise ValueError(F"path to the file {dict_file} does not exist!"				)
       __a       =    Dictionary.load(a				)
       __a       =    rewrite_dict_keys(src_dict.indices				)
       __a       =    len(a				)
       __a       =    os.path.join(a   ,    VOCAB_FILES_NAMES["vocab_file"]				)
       print(F"Generating {src_vocab_file} of {src_vocab_size} records"				)
       with open(a   ,    "w"   ,    encoding="utf-8"				) as f:
              f.write(json.dumps(a   ,    ensure_ascii=a   ,    indent=a				)				)

       # merges_file (bpecodes)
       __a       =    os.path.join(a   ,    "bpecodes"				)
       if not os.path.isfile(a				):
              raise ValueError(F"path to the file {bpecodes_file} does not exist!"				)

       __a       =    os.path.join(a   ,    VOCAB_FILES_NAMES["merges_file"]				)
       shutil.copyfile(a   ,    a				)

       # model config
       __a       =    os.path.join(a   ,    "config.json"				)

       __a       =    {
           "activation_dropout": args["activation_dropout"],
           "architectures": ["BioGptForCausalLM"],
           "attention_probs_dropout_prob": args["attention_dropout"],
           "bos_token_id": 0,
           "eos_token_id": 2,
           "hidden_act": args["activation_fn"],
           "hidden_dropout_prob": args["dropout"],
           "hidden_size": args["decoder_embed_dim"],
           "initializer_range": 0.02,
           "intermediate_size": args["decoder_ffn_embed_dim"],
           "layer_norm_eps": 1E-1_2,
           "layerdrop": args["decoder_layerdrop"],
           "max_position_embeddings": args["max_target_positions"],
           "model_type": "biogpt",
           "num_attention_heads": args["decoder_attention_heads"],
           "num_hidden_layers": args["decoder_layers"],
           "pad_token_id": 1,
           "scale_embedding": not args["no_scale_embedding"],
           "tie_word_embeddings": args["share_decoder_input_output_embed"],
           "vocab_size": src_vocab_size,
       }

       # good hparam defaults to start with

       print(F"Generating {biogpt_model_config_file}"				)
       with open(a   ,    "w"   ,    encoding="utf-8"				) as f:
              f.write(json.dumps(a   ,    ensure_ascii=a   ,    indent=a				)				)

       # tokenizer config
       __a       =    os.path.join(a   ,    a				)

       __a       =    {
           "bos_token": "<s>",
           "eos_token": "</s>",
           "model_max_length": 1_0_2_4,
           "pad_token": "<pad>",
           "special_tokens_map_file": None,
           "tokenizer_class": "BioGptTokenizer",
           "unk_token": "<unk>",
       }

       print(F"Generating {biogpt_tokenizer_config_file}"				)
       with open(a   ,    "w"   ,    encoding="utf-8"				) as f:
              f.write(json.dumps(a   ,    ensure_ascii=a   ,    indent=a				)				)

       # model
       __a       =    chkpt["model"]

       # remove unneeded keys
       __a       =    [
           "decoder.version",
       ]
       for k in ignore_keys:
              model_state_dict.pop(a   ,    a				)

       __a       =    list(model_state_dict.keys()				)
       for layer_name in layer_names:
              if layer_name.endswith("output_projection.weight"				):
                     __a       =    model_state_dict.pop(a				)
              else:
                     __a       =    model_state_dict.pop(a				)

       __a       =    BioGptConfig.from_pretrained(a				)
       __a       =    BioGptForCausalLM(a				)

       # check that it loads ok
       model_new.load_state_dict(a				)

       # save
       __a       =    os.path.join(a   ,    a				)
       print(F"Generating {pytorch_weights_dump_path}"				)
       torch.save(a   ,    a				)

       print("Conversion is done!"				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:Any									=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--biogpt_checkpoint_path""",
       default=None,
       type=str,
       required=True,
       help=(
           """Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
           """ bpecodes, etc."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   SCREAMING_SNAKE_CASE__:Any									=			parser.parse_args()
   convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)
